A new method called AgentGrad applies intervention-guided prompt optimization to large language model-based multi-agent systems, according to the paper's abstract. The approach targets the prompt design of each specialized agent, which the authors identify as the key determinant of multi-agent system performance. AgentGrad builds on textual gradient methods that guide prompt updates using natural-language feedback.
Large language model (LLM)-based multi-agent systems (MAS) achieve strong performance by employing specialized multiple agents, yet their performance depends on the prompt design of each agent. For MAS prompt optimization, textual gradient methods that guide prompt updates using natural-language fee
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